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Distributed PV Adoption in Maine through 2021

11/6/15

Pieter Gagnon and Ben Sigrin

Pieter.gagnon@nrel.gov, Benjamin.sigrin@nrel.gov

Team and Request Background

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Solar Technical Assistance Team (STAT)

Who are we: The Solar Technical Assistance Team (STAT) is a DOE-funded program that gathers NREL solar technology and deployment experts to provide unbiased information on solar policies and issues for state and local government decision makers. In general, the expert assistance is intended to support legislators, regulators, and their staff members in order to develop the market for solar photovoltaic (PV) technologies. For additional information, please visit: http://www.nrel.gov/tech_deployment/state_local_governments/stat.html

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Maine Public Utilities Commission Request

Maine PUC requested assistance from STAT. Based on scoping calls the assistance provided is as follows: NREL will produce an analysis through the dSolar model that addresses 3 potential distributed solar scenarios projecting low, medium, and high estimates of the total installed DG Solar PV through 2021 assuming no changes in policy concerning retail rate structures, net energy billing, or other solar incentives. NREL will consult with Maine PUC to develop a set of assumptions for each scenario, which could include: solar capital cost reductions, ITC step down scenarios, load and retail rate growth forecasts, and consumer demand for solar.

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Project Description

• NREL expert staff used the dSolar (distributed solar) model to generated low-medium-high estimates of distributed PV adoption in Maine through the year 2021.

• This slide deck presents a high-level overview of the model and modeling results. For complete documentation of the model, see The Distributed Generation Market Demand Model (dGen): Documentation (Sigrin et al., forthcoming December ‘15) and The Solar Deployment System (SolarDS) Model: Documentation (Denholm et al. 2009)

Model Background

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dSolar Model Description • The Distributed Generation Market

Demand model (dGen) is a National Renewable Energy Laboratory (NREL) tool used for predicting the adoption of behind-the-meter PV in the continental United States.

• dGen forecasts the adoption of PV based on the Diffusion of Innovations framework popularized by Bass (1969) and Rogers (2003). The framework posits that the diffusion of a novel technology occurs in an epidemic-like fashion, where the cumulative adoption pattern follows a logistic “S-curve”, as seen in the figure to the right. The curves shown are representative of the diffusion concept, and are not the shapes used in this analysis.

• For this analysis, the shape of the S-curve was tuned for both the residential and non-residential sectors in Maine, such that the curve matched historical PV adoption trends.

General Diffusion Trend

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dSolar Model Description

• dSolar uses the curves shown to characterize the relationship between PV’s economic attractiveness and its maximum market share. For example, with a 15-year payback, we predict 12% of possible residential customers and 1% of possible commercial and industrial customers would be willing to adopt solar PV.

• These figures set the upper bound of the S-curve curve (in red) of the previous slide. The model recalculates economic conditions for every 2 years in the forecast, and adjusts the shape of the curve (and therefore the rate of diffusion) accordingly.

• This method reflects the fact that system cost is the primary driver of PV adoption, while also capturing the non-economic considerations of customers. For example, even with long payback periods that would achieve lower rates of return than other potential investments, we would still expect a small percentage of possible customers to adopt PV. Conversely, even if the payback period is zero, we would expect a small number of eligible customers to still not adopt.

• The residential curve was generated by survey: Diffusion into New Markets: Economic Returns Required by Households to Adopt Rooftop Photovoltaics (Sigrin and Easan, 2014)

• The non-residential curve was generated through payback modeling: Rooftop Photovoltaics Market Penetration Scenarios (Paidipati et al., 2008)

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Modeling Assumptions and Results

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Scenario Assumptions

Scenario Installed PV cost

trajectory Retail electricity

price Load Growth

Investment Tax Credit (ITC) extension

Low PV adoption 50% Sunshot

AEO 2015 Low Economic Growth

Scenario No growth Expires as planned

Medium PV adoption - ITC Expires 62.5% Sunshot

AEO 2015 Reference Scenario AEO 2015 Reference Case

Expires as planned

Medium PV adoption - ITC Extends 62.5% Sunshot

AEO 2015 Reference Scenario AEO 2015 Reference Case

Extended through 2021

High PV adoption 75% Sunshot AEO 2015 Reference

Scenario AEO 2015 High Growth Case Extended through

2021

• The four varying parameters are detailed in the following slides. • All other assumptions held constant between scenarios (next slide)

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Fixed Assumptions

These assumptions are constant between the four scenarios: • Residential customers evaluate economics with a 20% down payment, and

finance the rest with a 15-year, 4.99% loan (PACE Loan) • Non-residential systems evaluate economics with a 40% equity, and finance the

rest with debt, a 20-year, 6.00% loan • Only owner-occupied buildings can adopt PV. • Currently net metering policies are active through 2021 in all scenarios • 5-year federal MACRS depreciation is available for non-residential customers.

Residential customers do not depreciate systems. • The maximum market share (which influences the rate of adoption) is based on

the market-share-vs-payback curves shown on slide 4. • PV payback periods are evaluated based on current rate tariffs in the 3 regions

and no new rate structures are evaluated. Current tariffs are scaled by forecasted electricity rate changes. Rate tariffs are assigned to customers based on their characteristics (for example, a customer with a maximum demand of 500 kW would not be eligible for CMP’s Medium General Service rate)

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Assumed PV Installed Price Trajectories

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62.5% Sunshot (Res)

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62.5% Sunshot (Non-Res)

75% Sunshot (Non-Res)

These are final installed prices of PV systems in real dollar terms. 2014 is the first simulated year, and is used to calibrate the model against historical adoption.

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Assumed Electricity Price Trajectories

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Residential Sector Electricity Price Trajectories Reference Scenario (Mid and HighAdoption)

Low Economic Growth Scenario(Low Adoption)

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Commercial Sector Electricity Price Trajectories

Reference Scenario (Mid and HighAdoption)

Low Economic Growth Scenario(Low Adoption)

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Industrial Sector Electricity Price Trajectories Reference Scenario (Mid and HighAdoption)

Low Economic Growth Scenario(Low Adoption)

The model uses current rate tariffs for CMP, EM-BHD, EM-MPD, assuming the Standard Offer is taken for electricity supply. We then scale the components of the tariffs based on two of the EIA’s AEO projections for New England. For example, we assume that the 2020 residential retail rates will be 113% of 2016 values in the reference case. Source: http://www.eia.gov/forecasts/aeo/

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Assumed Load Growth Trajectories

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High Economic Growth Scenario

No Growth

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High Economic Growth Scenario

No Growth

Load growth is modeled by adding/removing total customers, keeping per capita consumption constant. Source: http://www.eia.gov/forecasts/aeo/

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Solar Investment Tax Credit (ITC) Scenarios

ITC 'Expires' Scenario

Year Residential

Commercial, Industrial, and

Leased Systems 2016 30% 30% 2017 0% 10% 2018 0% 10% 2019 0% 10% 2020 0% 10% 2021 0% 10%

ITC 'Extends' Scenario

Year Residential

Commercial, Industrial, and

Leased Systems 2016 30% 30% 2017 30% 30% 2018 30% 30% 2019 30% 30% 2020 30% 30% 2021 30% 30%

• The residential ITC is currently set to expire to 0% at the end of 2016. The non-residential ITC is set to be reduced to 10% at the end of 2016.

• For the ‘High PV Adoption’ and ‘Medium PV Adoption with ITC Extension’ we assume the current 30% ITC is extended through 2021.

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PV System Design and Performance Assumptions

Characteristic Value System Size Sized to provide 95% of annual consumption, constrained by

available roof area Module Type Multicrystalline silicon Module Power Density 150 W/m2 Tilt Follows distribution of buildings characteristics in Maine

observed in LiDAR data Azimuth Follows distribution of buildings characteristics in Maine

observed in LiDAR data Ground Coverage Ratio 0.7 for systems on flat roofs. For systems on tilted roofs, PV

installed flush roofs Total System Losses 14.08% Module Degradation 0.5%/year Inverter Efficiency 95% Inverter Lifetime 10 years DC to AC Ratio 1.4

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Results

Scenario State-wide Installed Capacity of Distributed PV (MW) 2016 2017 2018 2019 2020 2021

High PV Adoption 25 40 54 89 124 189 Medium PV Adoption – ITC Extends 24 38 52 76 101 146 Medium PV Adoption – ITC Expires 24 32 39 56 72 97 Low PV Adoption 20 23 25 30 34 40

020406080

100120140160180200

2016 2017 2018 2019 2020 2021

Tota

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High PV AdoptionMedium PV Adoption - ITC ExtendedMedium PV Adoption - ITC ExpiresLow PV Adoption